Yesterday, in Lisbon, I defended my PhD thesis on Evolutionary Algorithms for dynamic optimization. The pdf of the document is here, and this is the abstract:
Many industrial applications have dynamic components that lead to variations of the fitness function and Genetic Algorithms (GAs) adaptiveness is an appropriate tool to solve this type of problems. The thesis proposes two new evolutionary methods to tackle dynamic problems. The first acts upon mating and avoids crossover between similar individuals, via a self-regulated mechanism, thus preserving genetic diversity. The second is a new mutation operator able to evolve self-regulated mutation rates with a particular distribution that is suited for dynamic optimization. Finally, an efficient hybrid method that combines both strategies is proposed. The objective and main claim is the possibility of designing nature-inspired protocols for GAs that are efficient when evolving on dynamic environments while preserving algorithms’ complexity and not requiring a priori information about the problem.
The proposals are tested on a wide range of problems and are able to outperform frequently other GAs, namely when the frequency of change is lower. The hybrid scheme proves to be particularly effective since it broadened the range of dynamics in which each method by itself excels. As projected, the proposed techniques are robust and do not increase parameters’ set, thus fulfilling necessary conditions for real-world applications.